PUB-550 Data Management and Descriptive Statistics DQ Topics

PUB-550 Data Management and Descriptive Statistics DQ Topics

Topic 1: Data Management and Descriptive Statistics

ORDER A PLAGIARISM-FREE PAPER HERE

Topic 1 DQ 1

Mixed methods research refers to an emergent methodology of research that advances the systematic integration or mixing of quantitative and qualitative data within a single investigation or sustained program of inquiry. The basic premise of this methodology is that such integration permits a more complete and synergistic utilization of data than do separate quantitative and qualitative data collection and analysis. It allows qualitative and quantitative research to complement each other. PUB-550 Data Management and Descriptive Statistics DQ Topics. It originated in the social sciences and has recently expanded into the health and medical sciences including fields such as nursing, family medicine, social work, mental health, pharmacy, allied health, and others (Wisdom & Creswell, 2013). Mixed method research can facilitate the involvement of other key stakeholders, such as partners, family members, and/or other knowledge users, in the process of developing the research question(s) and outlining the research designs. MMR is a strong option to leverage effective patient engagement and support ongoing research focused on patient-identified priorities and the improvement of patient outcomes (Regnault et al., 2018).

Quantitative research is expressed in numbers and graphs. It is used to test or confirm theories and assumptions. This type of research can be used to establish generalizable facts about a topic. Requires many respondents, focuses on testing theories and hypotheses, and uses closed (multiple choice) questions. The Key terms for quantitative research are testing, measurement, objectivity, replicability (Streefkerk, 2019). PUB-550 Data Management and Descriptive Statistics DQ Topics

Qualitative research is expressed in words. It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood. It focuses on exploring ideas and formulating theory or hypothesis, it requires few respondents with open-ended questions. It is analyzed by summarizing, categorizing, and interpreting information. The key terms here are understanding, context, complexity, and subjectivity (Streefkerk, 2019). PUB-550 Data Management and Descriptive Statistics DQ Topics

 

References:

Streefkerk, R. (2019). The differences between quantitative and qualitative research. Retrieved from

https://www.scribbr.com/methodology/qualitative-quantitative-research/

 

Regnault, A., Willgoss, T., Barbic, S. (2018). Towards the use of mixed methods inquiry as best practice in health outcomes research. Retrieved from

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5934918/#__ffn_sectitle

 

Wisdom, J. and Creswell, J. W. (2013). Mixed Methods: Integrating Quantitative and Qualitative Data Collection and Analysis While Studying Patient-Centered Medical Home Models. Retrieved from

https://pcmh.ahrq.gov/page/mixed-methods-integrating-quantitative-and-qualitative-data-collection-and-analysis-while

 

 

Topic 1 DQ 1

Mixed methods research was originally designed for researching social sciences questions, however in recent years has expanded into the health and medical sciences research. Mixed method research combines the best parts of qualitative and quantitative research without the inevitable bias of each (Almaki, 2016). Qualitative research is non-numerical, scientific research involving observation, that is inductive and used by investigators to develop hypotheses (Wisdom & Creswell, 2013) PUB-550 Data Management and Descriptive Statistics DQ Topics. Inductive involves drawing meaning from those that are participating in the study. Qualitative falls victim to bias as it is difficult separate opinion or researcher bias with this method (Almaki, 2016). Quantitative on the other hand involves quantifiable numerical data used to test hypotheses. It is a deductive research method, meant to be used to collect data in regards to hypotheses developed investigating relationships between variables (Almaki, 2016). Mixed methods research allows for each qualitative and quantitative to make up for eachother’s weaknesses in integrating methodologies from both methods, allowing for greater understanding. According to Wisdom and Creswell (2013) the core characteristics of a well-designed mixed methods study is one that collects and analyzes both quantitative and qualitative data, uses rigorous procedures in process to ensure appropriate sample size for quantitative and qualitative analysis, integrates the data during data collection, analysis, and discussion, and implements both methods components either at the same time or sequentially. PUB-550 Data Management and Descriptive Statistics DQ Topics.

There are different mixed methods designs that can be used by investigators, and which method is utilized may depend on the research question and resources available. There are explanatory, exploratory, embedded, and convergent (also referred to as triangulation). Explanatory occurs in two phases, an initial quantitative followed by qualitative data collection, allowing for quantitative results to be explained and understood through the personal experiences and explanation of qualitative data (Wisdom and Cresswell, 2013). This method is easy to apply, and allows for on type of data to complement and build on the other. Exploratory design is the reverse of explanatory as it collects qualitative data before quantitative. Qualitative data is collected through interviews and through analyzing the information an appropriate quantitative data collection can be performed (Alamaki, 2016). Embedded studies are augmentative studies, with one method playing a secondary role to the other PUB-550 Data Management and Descriptive Statistics DQ Topics. The benefits of embedded studies are that it is easier to implement and requires less resources, but there are challenges in integrating results (Alamaki, 2016). Triangulation, or convergent design, involves collecting both types of data at the same time on the same topic and then integrating for analysis and interpretation (Alamaki, 2016). The two types of data can compliment and validate one another, however it can be challenging to integrate two different methods.

 

References

Almalki, S. (2016). Integrating quantitative and qualitative data in mixed methods research-challenges and benefits. Journal of Education and Learning, 5(3), 288-296. doi: 10.5539/jel.v5n3p288

Wisdom, J. & Creswell, J. W. (2013). Mixed methods: Integrating quantitative and qualitative data collection and analysis while studying patient-centered medical home models (AHRQ Publication No. 13-0028-EF). Retrieved from https://pcmh.ahrq.gov/page/mixed-methods-integrating-quantitative-and-qualitative-data-collection-and-analysis-while#h=Mixed%20methods   PUB-550 Data Management and Descriptive Statistics DQ Topics

 

Topic 1 DQ 2

Comprehending the foundation for hypothesis testing and other types of inferential statistics allows the individual to understand how both sample and population are different.

Statistics(n.d.) study found the following: A population is a collection of people, items, or events about which you want to make inferences. It is not always convenient or possible to examine every member of an entire population. For example, it is not practical to count the bruises on all apples picked at an orchard. It is possible, however, to count the bruises on a set of apples taken from that population. This subset of the population is called a sample (p.n.d.) PUB-550 Data Management and Descriptive Statistics DQ Topics.

However, when differentiating between Statistics and Parameter is both measure testing differently for example, Descriptive Statistics describe a sample whereas parameter describe an entire population.

According to academy lessons (n.d.), an example of a descriptive statistic will be a pet shop that sells cats, dogs, birds and fish. If 100 pets are sold, and 40 out of the 100 were dogs, then one description of data on pets sold would be that 40% were dogs. However, in inferential statistics an example is wanting to know the height of all the men in a city with a population of millions of residents (p.n.d.) PUB-550 Data Management and Descriptive Statistics DQ Topics.

According to Page (2014), Evidence‐based practice is supposed to affect clinical decision‐making, but interpreting research is often difficult for some clinicians. Clinical interpretation of research on treatment outcomes is important because of its influence on clinical decision‐making including patient safety and efficacy (p.726).

Reference

Page P. (2014). Beyond statistical significance: clinical interpretation of rehabilitation research literature. International journal of sports physical therapy, 9(5), 726–736.

https://www.statisticshowto.datasciencecentral.com/how-to-tell-the-difference-between…

https://study.com/academy/lesson/descriptive-and-inferential-statistics.html

 

https://support.minitab.com/…/statistics/…statistics/…/how-are-population-and-sample-.

 

Topic 1 DQ 2

In a research study, participants are selected from a population, this is the larger group of cases a researcher is interested in studying (Corty, 2016). For example, a study researching exercise regimes uses all members from three local gyms. This would be the total population for the research study. Even after a specific population in a community is defined, it is impossible for the researcher to locate all subjects, for this reason, researchers conduct their research on a portion of the population, known as a sample (Corty, 2016). A sample is a small group of cases chosen within a population (Corty, 2016). It is called a representative sample if it accurately reflects the larger population (Corty, 2016) PUB-550 Data Management and Descriptive Statistics DQ Topics. Using the same example, the sample would be select members of both male and female adults for the exercise study.

From a sample or population, a single number is formed such as an average. This number is used to summarize the group (Corty, 2016). It is known as a statistic if the number represents the sample and it is known as a parameter if the number represents a population (Corty, 2016). For example, if you wanted to know the mean income of the local community, it is a parameter of a population. When you draw a random sample of 200 community residents and determine the mean, that number would be a statistic. Most likely, you can conclude the population mean will be close to that same number PUB-550 Data Management and Descriptive Statistics DQ Topics.

Descriptive statistics can be used to characterize a population or sample. It is a summary statement about of a set of cases (Corty, 2016). It involves reducing a set of data to a value (Corty, 2016). For example, if an individual reported 55% of the workplace is male, that would be a descriptive statistic. Inferential statistics can be used when describing the larger population (Corty, 2016). An inferential statistic also reduces data to a single value, but it is accomplished to make inferences about a population (Corty, 2016). This is why it is critical the sample represents the population. For example, you could stand in a mall and ask 200 people if they like shopping at a specific store. You then would use that information to reason that around 85% of people like shopping at that store. It is necessary to estimate parameters in inferential statistics. This means using a statistic from the same information and using it to for the population mean (Glen, 2014). PUB-550 Data Management and Descriptive Statistics DQ Topics.

It is critical to be able to differentiate sample and population and parameter and a statistic in public health research. Public health is concerned with protecting the health of entire populations. These populations can be as small as a local neighborhood or as big as the entire world (CDC Foundation, 2020). In order to properly assess the health of big and small population, samples of populations must be drawn using specific parameters and statistics.

References

Centers for Disease Control and Prevention (CDC) Foundation. Public Health In Action. Retrieved from https://www.cdcfoundation.org/what-public-health

Corty, E.W. (2016). Using and Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences. (3^rd Ed.) New York, NY: Worth Publishers

Glen, S. (2014). What is Inferential Statistics? Retrieved from https://www.statisticshowto.datasciencecentral.com/inferential-statistics/ PUB-550 Data Management and Descriptive Statistics DQ Topics